A surrogate Bayesian framework for a SARS-CoV-2 data driven stochastic model

Articles in the same Issue

1. COVID-19 and Dynamics of Infectious Diseases
2. Modeling, Analysis and Physics Informed Neural Network approaches for studying the dynamics of COVID-19 involving human-human and human-pathogen interaction
3. A surrogate Bayesian framework for a SARS-CoV-2 data driven stochastic model
4. Global stability dynamics and sensitivity assessment of COVID-19 with timely-delayed diagnosis in Ghana
5. Modeling for COVID-19 Decision Making in Health Policy Evaluation
6. A systematic review, meta-analysis and economic evaluation on Neonatal cpap
7. A Mathematical Model for Blood Flow Accounting for the Hematological Disorders
8. Modeling and prediction of the third wave of COVID-19 spread in India
9. Age Structured Mathematical Modeling Studies on COVID-19 with respect to Combined Vaccination and Medical Treatment Strategies
10. Regular Articles
11. Reaction Rate Theory-Based Mathematical Approximation for the Amount of Time it Takes For Cellular Respiration to Occur
12. Evolutionary trend of SARS-CoV-2 inferred by the homopolymeric nucleotide repeats
13. Derangement model of ligand-receptor binding
14. Impact of disease on a two-patch eco-epidemic model in presence of prey dispersal
15. Optimal Control for a Tuberculosis Model with Exogenous Reinfection under the Influence of Stigma
16. Neighborhood hypergraph model for topological data analysis
17. Regulation of Lysosomal Associated Membrane Protein 3 (LAMP3) in Lung Epithelial Cells by Coronaviruses (SARS-CoV-1/2) and Type I Interferon Signaling